User Authentication Recognition Process Using Long Short-Term Memory Model

User authentication (UA) is the process by which biometric techniques are used a person to gain access physical or virtual site. UA has been implemented in various applications such as financial transactions, data privacy, and control. Various techniques, facial fingerprint recognition, have proposed for healthcare monitoring address recognition problems. Photoplethysmography (PPG) technology an optical sensing technique collects volumetric blood change from subject’s skin near fingertips, earlobes, forehead. PPG signals can be readily acquired devices smartphones, smartwatches, web cameras. Classical machine learning decision trees, support vector (SVM), k-nearest neighbor (kNN), identification. We developed classification method smart using long short-term memory (LSTM). Specifically, our classifier algorithm uses raw so not lose specific characteristics of signal coming each user’s behavior. In context, false positive negative rates crucial. recruited thirty healthy subjects smartphone take data. Experimental results show that Bi-LSTM-based based on feature-based data-based deep approaches provides 95.0% 96.7% accuracy, respectively.